Numeric vectors that represent the meaning of text (or images), so that similar content sits close together — the backbone of semantic search and RAG.
An embedding model turns a piece of text into a fixed-length list of numbers (a vector) that captures its meaning. Texts about similar topics produce vectors that are close together, so you can measure similarity by comparing vectors instead of matching keywords.
This powers semantic search, recommendations, clustering, and especially Retrieval-Augmented Generation (RAG): you embed your documents, store the vectors in a database, embed the user's question, retrieve the closest chunks, and feed them into a chat model's context. Embedding models are usually small, cheap, and fast compared to generative LLMs.
Embeddings are a different job from text generation — an embedding model outputs vectors, not sentences. In Spanvero's catalog these are tagged (e.g. "embedding") so you can find them distinctly from chat models.
Tokens · Context window · Fine-tuning · Inference
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